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O. Isayev

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#artificial intelligence Preprint Sep 2026

EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights

When Isaac Newton discovered the law of gravitation, he did so through an iterative process of analyzing observed data such as planetary patterns, finding the underlying mechanisms by describing patterns in mathematical equations, and refining his theory against the Moon's orbit, revealing the startling insight that th...

Jia-Yi Geng, Zhengxuan Wu, Kevin S. Chen et al. · 0 citations
Book Open access Aug 2026

From VAEs to Diffusion and LLMs: Modern Generative Models for Molecular Discovery

This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures.

Ke-Han Guo, Yili Shen, Jeehyun Hwang et al. · 0 citations
Open access Sep 2026

Critical benchmarking of machine-learned interatomic potentials for intermolecular and noncovalent interactions

Accurate benchmarking of intermolecular interaction energies is central to evaluating quantum chemical methods and guiding the development of reliable machine-learned interatomic potentials (MLIPs) for chemical and biological applications. In this work, we benchmark five MLIPs, namely AIMNet2(2023), AIMNet2(2025), MACE...

Kamal Singh Nayal, Ilkwon Cho, O. Isayev · 0 citations
Aug 2026

Predicting Copolymerization Reactivity Ratios: Do We Need Better Models or Better Data?

Reactivity ratios are a key metric for understanding copolymer microstructure, yet they are challenging to predict a priori. Data-driven methods have recently been employed for the prediction of free radical copolymerization reactivity ratios, but model extrapolation remains modest with respect to accuracy. This has...

Caroline M. Coxwell, Dylan M Anstine, O. Isayev et al. · 0 citations
Jul 2026

Electron Alchemy with Machine-Learned Interatomic Potentials: Case Studies of Local Charge in Bond Dissociation Curves.

The convergence of molecular dynamics simulations and machine-learned interatomic potentials (MLIPs) promises density functional theory (DFT) level accuracy at near-classical force-field computational costs. However, while the average fidelity to reference energies and forces approaches perfection, several failure mode...

Ericka Roy Miller, V. Sathyaseelan, Dylan M Gilley et al. · 0 citations
Book Open access Aug 2026

From VAEs to Diffusion and LLMs: Modern Generative Models for Molecular Discovery

This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures.

Kehan Guo, Yili Shen, Jeeyhun Hwang et al. · 0 citations
Preprint Aug 2026

MolecularCanvas: LLM-assisted Small-Molecule Drug Discovery via Structure-Guided Constraints

MolecularCanvas is an interactive system that enables users to iteratively construct an optimization context by integrating high-level goals, structure-level annotations, property constraints, and reference-based preferences that guides the generation of candidate molecules across diverse molecular structures.

Haoyu Dong, Rui Sheng, Shu-Hao Zhang et al. · 0 citations
Open access Jul 2026

Machine learning-accelerated screening of hydroquinone analogs for proton-coupled electron transfer

Proton-coupled electron transfer (PCET) mediated by hydroquinone and related molecules is key to natural and artificial energy conversion. The reactivity of these molecules depends on their bond dissociation free energy (BDFE), but studying the relationship between structure and thermochemistry across this chemical spa...

Rajdeep Sarma, Yiwen Wang, David D Hebert et al. · 0 citations

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